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    Hybrid model for learner modelling and feedback prioritisation in exploratory learning

    Cocea, Mihaela and Magoulas, George D. (2009) Hybrid model for learner modelling and feedback prioritisation in exploratory learning. International Journal of Hybrid Intelligent Systems 6 (4), pp. 211-230. ISSN 1448-5869.

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    Individual and/or hybrid AI techniques are often used in learning environments for well-structured domains to perform learner diagnosis, create and update a learner model and provide support at individual or group level. This paper presents a conceptual model that employs a synergistic approach based on Case-Based Reasoning (CBR) and Multicriteria Decision Making (MDM) components for learner modelling and feedback generation during exploration in an ill-defined domain of mathematical generalisation. The CBR component is used to diagnose what students are doing on the basis of simple and composite cases; simple cases represent parts of the models that the learners could possibly construct during an exploratory learning activity, while composite cases, which are assembled from simple cases, correspond to strategies that learners may adopt to construct their models. Similarity measures are used to identify how close/far are the learners from solutions pre-specified and stored in the knowledge base. This information is then fed into the MDM component that is responsible for prioritising types of feedback depending on the context. The operation of the two components and the effectiveness of the synergistic approach are validated through user scenarios in the context of an exploratory learning environment for mathematical generalisation.


    Item Type: Article
    Keyword(s) / Subject(s): Hybrid methods, learner modelling, case-based reasoning, analytic hierarchy process, exploratory learning
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Research Centres and Institutes: Birkbeck Knowledge Lab
    Depositing User: Administrator
    Date Deposited: 04 Feb 2011 15:55
    Last Modified: 09 Aug 2023 12:30


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